42 citations · 53 across the 5 of their papers we have counts for
5 papers
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Alexandre Drouin, Sébastien Giguère, Maxime Déraspe +3
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers. We extend the SCM for datasets that contain a huge number of features. The whole gen…
On the String Kernel Pre-Image Problem with Applications in Drug Discovery
Sébastien Giguère, Amélie Rolland, François Laviolette +1
The pre-image problem has to be solved during inference by most structured output predictors. For string kernels, this problem corresponds to finding the string associated to a giv…
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
Alexandre Drouin, Sébastien Giguère, Vladana Sagatovich +4
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes…
Improved design and screening of high bioactivity peptides for drug discovery
Sébastien Giguère, François Laviolette, Mario Marchand +4
The discovery of peptides having high biological activity is very challenging mainly because there is an enormous diversity of compounds and only a minority have the desired proper…
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Sébastien Giguère, Mario Marchand, François Laviolette +2
We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino ac…